docs: minor fixes in links (#104)

* docs: fix minor issues in basics section

* docs: fix minor issues in filters section

* docs: fix minor issues in comparison section

* docs: fix minor issues in geometry section

* docs: minor fixes in morphology section

* docs: minor fixes in operations section

* docs: minor fixes in roi analysis section

* docs: minor fixes in tips section

* docs: minor fixes in tutorials section

close: #81

* docs: remove invalid link

* docs: fix prettier error

Author: EscapedGibbon

docs/Basics/Working with Images.md

@@ -55,7 +55,7 @@ In ImageJS main properties of an image are:
 
 - [Alpha channel](../Glossary.md#alpha-channel 'internal link on alpha-channel'): channel that represents the transparency or opacity levels of pixels.
 
-- [Metadata](../Glossary.md#metadata 'internal link on metadata'): data about data. A basic example would be date and time when an image was taken
+- [Metadata](../Glossary.md#metadata 'internal link on metadata'): data about data. A basic example would be date and time when an image was taken.
 
 ### Features
 

docs/Basics/Working with Masks.md

@@ -37,7 +37,7 @@ In most cases, thresholding is your go-to method to get a mask from an image.
 
 ### Use `cannyEdgeDetector()` method
 
-There is also a third way to get a mask. It is to use [`cannyEdgeDetector` method](../Features/Morphology/Canny%20Edge%20Detector.md).
+There is also a third way to get a mask. It is to use [`cannyEdgeDetector` method](../Features/Morphology/Canny%20Edge%20Detector.md 'internal link on canny edge detector').
 
 ```ts
 const mask = image.cannyEdgeDetector(); // returns a mask

docs/Basics/Working with ROIs.md

@@ -4,7 +4,7 @@ There are currently two ways ROIs can be generated in ImageJS:
 
 - From [masks](./Working%20with%20Masks.md 'internal link on working with mask') by identifying contiguous black or white pixels within it.
 <!-- TODO: add links to the relevant sections once they exist -->
-- By identifying starting points of interest (for example by finding and filtering local extrema) and running the watershed algorithm on them.
+- By identifying starting points of interest (for example by finding and filtering local extrema) and running the [watershed algorithm](../Features/Operations/Watershed.md 'internal link on watershed') on them.
 
 ROIs identify and characterize regions within images, which has wide applications in image analysis.
 

docs/Features/Comparison/Addition.md

@@ -4,7 +4,7 @@ sidebar_position: 10
 
 _Makes an addition between two images._
 
-[Check options and parameters of `add` method](https://image-js.github.io/image-js-typescript/classes/Image.html#add 'github.io link')
+[🖼️ Image options and parameters of `add` method](https://image-js.github.io/image-js-typescript/classes/Image.html#add 'github.io link')
 
 `add` method, opposed to [subtraction](./Subtraction.md 'internal link on subtract'), takes another Image and makes an addition between each respective pixel value.
 It works like this:
@@ -34,4 +34,4 @@ Images must have the same size, channel number and bit depth for compatibility r
 
 ### Parameters and default values
 
-- [`otherImage`](https://image-js.github.io/image-js-typescript/classes/Mask.html#subtract 'github.io link')
+- [`otherImage`](https://image-js.github.io/image-js-typescript/classes/Mask.html#add 'github.io link')

docs/Features/Comparison/Comparison.md

@@ -10,7 +10,7 @@ The specific reasons for comparing images depend on the application and the desi
 | Can be applied on                                                               | Images   | Masks    |
 | ------------------------------------------------------------------------------- | -------- | -------- |
 | [Addition(`add`)](./Addition.md 'internal link on add')                         | ✅  | ❌ |
-| [Subtraction( `subtract` )](./Subtraction.md 'internal link on subtract')       | ✅  | ✅  |
-| [Hypotenuse( `hypotenuse` )](./Hypotenuse.md 'internal link on hypotenuse')     | ✅  | ❌ |
+| [Subtraction(`subtract`)](./Subtraction.md 'internal link on subtract')         | ✅  | ✅  |
+| [Hypotenuse(`hypotenuse`)](./Hypotenuse.md 'internal link on hypotenuse')       | ✅  | ❌ |
 | [Logical conjunction(`and`)](./Logical%20conjunction.md 'internal link on and') | ❌ | ✅  |
 | [Logical disjunction(`or`)](./Logical%20disjunction.md 'internal link on or')   | ❌ | ✅  |

docs/Features/Comparison/Hypotenuse.md

@@ -30,10 +30,9 @@ Where $$Value1$$ is a value of the pixel in the first image and $$Value2$$ is th
 
 #### Options
 
-| Property                                                                                                | Required | Default value    |
-| ------------------------------------------------------------------------------------------------------- | -------- | ---------------- |
-| [`bitDepth`](https://image-js.github.io/image-js-typescript/interfaces/HypotenuseOptions.html#bitDepth) | no       | `image.bitDepth` |
-| [`channels`](https://image-js.github.io/image-js-typescript/interfaces/HypotenuseOptions.html#channels) | no       | -                |
+| Property                                                                                                | Required | Default value |
+| ------------------------------------------------------------------------------------------------------- | -------- | ------------- |
+| [`channels`](https://image-js.github.io/image-js-typescript/interfaces/HypotenuseOptions.html#channels) | no       | -             |
 
 :::caution
 Images must be compatible by size, bit depth, number of channels and number of alpha channels. However, for the resulting image user can choose the bit depth of the resulting image as well as channels which the algorithm will be applied to.

docs/Features/Filters/Blur.md

@@ -12,7 +12,7 @@ Blur, also known as average blur or box blur, is a simple image processing techn
 
 <BlurDemo />
 
-Box blur is particularly effective in reducing [salt-and-pepper](https://en.wikipedia.org/wiki/Salt-and-pepper_noise 'wikipedia link on salt and pepper noise') noise (random black and white pixels) and minor imperfections in an image. However, it also leads to loss of finer details, so the choice of [kernel](../../Glossary.md#kernel) size is important.
+Box blur is particularly effective in reducing [salt-and-pepper](https://en.wikipedia.org/wiki/Salt-and-pepper_noise 'wikipedia link on salt and pepper noise') noise (random black and white pixels) and minor imperfections in an image. However, it also leads to loss of finer details, so the choice of [kernel](../../Glossary.md#kernel 'glossary link on kernel') size is important.
 More advanced blurring techniques, such as [Gaussian blur](./Gaussian%20Blur.md 'internal link to gaussian blur') or [bilateral filter](https://en.wikipedia.org/wiki/Bilateral_filter 'wikipedia link on bilateral filters'), are often used for better results in various applications.
 
 ### Kinds of images compatible with algorithm
@@ -46,7 +46,7 @@ Here's how blur filter is implemented in ImageJS:
 
 _Select a Kernel Size_: The first step is to choose the size of the kernel or window that will be used for the blurring operation. The kernel is typically a square matrix with odd dimensions, such as 3x3, 5x5, 7x7, etc. The larger the kernel, the more intense the blurring effect.
 
-_Iterate through Pixels_: For each pixel in the image, the algorithm applies [convolution](../../Glossary.md#convolution).
+_Iterate through Pixels_: For each pixel in the image, the algorithm applies [convolution](../../Glossary.md#convolution 'glossary link on convolution').
 
 _Calculate Average Color_: The algorithm calculates the average color value of all the pixels within the kernel.
 

docs/Features/Filters/Filters.md

@@ -8,9 +8,9 @@ sidebar_position: 0
 
 | Can be applied on                                                                | Images  | Masks    |
 | -------------------------------------------------------------------------------- | ------- | -------- |
-| [Invert(`add`)](./Invert.md 'internal link on invert')                           | ✅ | ✅  |
-| [Grayscale( `grey` )](./Grayscale.md 'internal link on grayscale')               | ✅ | ❌ |
-| [Gradient(`gradient` )](./Gradient.md 'internal link on gradient')               | ✅ | ❌ |
+| [Invert(`invert`)](./Invert.md 'internal link on invert')                        | ✅ | ✅  |
+| [Grayscale(`grey`)](./Grayscale.md 'internal link on grayscale')                 | ✅ | ❌ |
+| [Gradient(`gradient`)](./Gradient.md 'internal link on gradient')                | ✅ | ❌ |
 | [Derivative(`derivative`)](./Derivative.md 'internal link on derivative')        | ✅ | ❌ |
 | [Median(`median`)](./Median.md 'internal link on median')                        | ✅ | ❌ |
 | [Pixelate(`pixelate`)](./Pixelate.md 'internal link on pixelate')                | ✅ | ❌ |

docs/Features/Filters/Gaussian Blur.md

@@ -8,7 +8,7 @@ _A blur filter that uses gaussian function for smoothing._
 
 [🖼️ Image options and parameters of `gaussianBlur` method](https://image-js.github.io/image-js-typescript/classes/Image.html#gaussianBlur 'link on github io')
 
-[Gaussian blur](https://en.wikipedia.org/wiki/Gaussian_blur 'Wikipedia link on gaussian blur') is a widely used image processing technique that smooths an image by reducing high-frequency noise and fine details while preserving the overall structure and larger features. It's named after the [Gaussian function](https://en.wikipedia.org/wiki/Gaussian_function 'wikipedia link on Gaussian function'), which is a mathematical function that represents a bell-shaped curve. Gaussian blur is often applied to images before other processing steps like edge detection to improve their quality and reliability.
+[Gaussian blur](https://en.wikipedia.org/wiki/Gaussian_blur 'Wikipedia link on gaussian blur') is a widely used image processing technique that smooths an image by reducing high-frequency noise and fine details while preserving the overall structure and larger features. It's named after the [Gaussian function](https://en.wikipedia.org/wiki/Gaussian_function 'wikipedia link on gaussian function'), which is a mathematical function that represents a bell-shaped curve. Gaussian blur is often applied to images before other processing steps like edge detection to improve their quality and reliability.
 
 The key idea behind Gaussian blur is that it simulates a diffusion process, where each pixel's value is influenced by the values of its neighbors. Because the weights are determined by the Gaussian function, pixels that are closer to the central pixel have a larger impact on the smoothed value, while pixels that are farther away contribute less.
 
@@ -48,7 +48,7 @@ With Gaussian blur there are two ways of passing options: through sigma and thro
 | [`borderType`](https://image-js.github.io/image-js-typescript/interfaces/GaussianBlurXYOptions.html#borderType) | no       | `reflect101`                    |
 | [`out`](https://image-js.github.io/image-js-typescript/interfaces/GaussianBlurXYOptions.html#out)               | no       | -                               |
 | [`sizeX`](https://image-js.github.io/image-js-typescript/interfaces/GaussianBlurXYOptions.html#sizeX)           | no       | `2 * Math.ceil(2 * sigmaX) + 1` |
-| [`sizeX`](https://image-js.github.io/image-js-typescript/interfaces/GaussianBlurXYOptions.html#sizeY)           | no       | `2 * Math.ceil(2 * sigmaY) + 1` |
+| [`sizeY`](https://image-js.github.io/image-js-typescript/interfaces/GaussianBlurXYOptions.html#sizeY)           | no       | `2 * Math.ceil(2 * sigmaY) + 1` |
 
 The size of the Gaussian kernel and the standard deviation parameter (which controls the spread of the Gaussian curve) influence the degree of smoothing. A larger kernel or a higher standard deviation will produce more pronounced smoothing, but might also result in a loss of fine details.
 

docs/Features/Filters/Gradient.md

@@ -6,9 +6,9 @@ import GradientDemo from './demos/gradient.demo.tsx'
 
 _Highlights changes in color or intensity within an image by accentuating transitions and edges._
 
-[🖼️ Image options and parameters of `gradient` method](https://image-js.github.io/image-js-typescript/functions/gradientFilter.html 'link on github.io')
+[🖼️ Image options and parameters of `gradient` method](https://image-js.github.io/image-js-typescript/classes/Image.html#gradientFilter 'link on github.io')
 
-Gradient filter or specifically[ a gradient-based edge detection filter](https://en.wikipedia.org/wiki/Graduated_neutral-density_filter 'Wikipedia link on gradient filter'), is an image processing technique used to highlight edges and boundaries within an image by emphasizing areas of rapid intensity change. The gradient filter operates by calculating the rate of change of pixel intensities across the image. When there's a rapid transition from one intensity level to another, [the convolution operation](../../Glossary.md#convolution 'glossary link on convolution') captures this change as a high gradient magnitude value, indicating the presence of an edge. It's a fundamental step in various computer vision and image analysis tasks, such as edge detection, object recognition, and image segmentation.
+Gradient filter or specifically[ a gradient-based edge detection filter](https://en.wikipedia.org/wiki/Graduated_neutral-density_filter 'wikipedia link on gradient filter'), is an image processing technique used to highlight edges and boundaries within an image by emphasizing areas of rapid intensity change. The gradient filter operates by calculating the rate of change of pixel intensities across the image. When there's a rapid transition from one intensity level to another, [the convolution operation](../../Glossary.md#convolution 'glossary link on convolution') captures this change as a high gradient magnitude value, indicating the presence of an edge. It's a fundamental step in various computer vision and image analysis tasks, such as edge detection, object recognition, and image segmentation.
 
 <GradientDemo />