feat(v4): add docker-compose and production-ready application

README.md

@@ -2,6 +2,8 @@
 
 Production-grade public SPA for spatial-domain image enhancement using Django REST Framework, OpenCV, NumPy, React, Tailwind CSS, Celery, Redis, PostgreSQL, Docker Compose, and Caddy.
 
+![screenshot](./screenshot.png)
+
 ## Local Development
 
 Backend:
@@ -48,6 +50,48 @@ The Django app follows the HackSoftware Django Styleguide pattern:
 - `processing/selectors.py` contains database fetch helpers.
 - Settings are environment-driven through `backend/.env`.
 
+## Algorithms
+
+The app is organized as a small MATLAB-like image workspace. Each operation creates a new image state, so you can compare results, keep useful steps, and delete unwanted states.
+
+### Basic Workspace
+
+- **Histogram view** shows how pixel values are distributed. For gray images it uses one intensity histogram; for RGB images it also shows R, G, and B channels. Formula: `p(r_k) = n_k / n`.
+- **Histogram equalization** improves contrast by spreading gray levels using the cumulative histogram. Formula: `s_k = round(255 * CDF(r_k))`.
+- **Add images** combines registered images by summing pixels and clipping to display range. Formula: `g = f1 + f2`.
+- **Subtract images** highlights differences between registered images. Formula: `g = normalize(|f1 - f2|)`.
+- **Dot product** multiplies registered image pixels element by element. Formula: `g = normalize(f1 * f2)`.
+- **Average K images** reduces independent noise by averaging registered states. Formula: `g = (1/K) * sum(f_i)`.
+
+### Chapter 3: Spatial Domain
+
+- **Negative** inverts intensities. Formula: `s = 255 - r`.
+- **Log transform** expands darker values more than brighter values. Formula: `s = c log(1 + r)`.
+- **Power-law / gamma** changes brightness and contrast with an exponent. Formula: `s = c r^gamma`.
+- **Gray-level dynamic range** stretches a selected intensity range to the full display range. Formula: `[low, high] -> [0, 255]`.
+- **Gray-level slicing** highlights pixels inside a chosen range. Formula: highlight where `A <= r <= B`.
+- **Bit-plane slicing** displays one binary bit of each gray value. Formula: `bit_k(r)`.
+- **Noise filter** adds test noise. Gaussian noise uses `g = f + n`; salt-and-pepper noise randomly sets pixels to `0` or `255`.
+- **Average N noisy copies** generates `N` independent Gaussian-noisy copies of the current image and averages them into one result. Formula: `result = (1/N) * sum_i(f + n_i)`.
+- **Average / box filter** smooths an image with a uniform mask. Formula: `g = imfilter(f, ones(K,K) / K^2)`.
+- **Weighted average filter** smooths with the slide mask `1/16 * [[1,2,1],[2,4,2],[1,2,1]]`.
+- **Gaussian filter** smooths using a Gaussian mask controlled by size `K` and variance `Q`. Formula: `G(x,y) = exp(-(x^2+y^2)/(2Q))`.
+- **Median filter** replaces each pixel with the neighborhood median, useful for salt-and-pepper noise. Formula: `g(x,y) = median(S_xy)`.
+- **Max filter** replaces each pixel with the local maximum. Formula: `g(x,y) = max(S_xy)`.
+- **Min filter** replaces each pixel with the local minimum. Formula: `g(x,y) = min(S_xy)`.
+- **Laplacian sharpening masks** use the taught cross or diagonal sharpening masks to emphasize fine detail. Formula: `g = imfilter(f, selected mask)`.
+- **Gradient operators** use Sobel or Roberts mask pairs for edges. Formula: `g = |imfilter(f,Gx)| + |imfilter(f,Gy)|`.
+- **High-boost / edge emphasis** sharpens by subtracting a blurred image from an amplified original. Formula: `f_hb = A f - blurred(f)`, where `A >= 1`.
+
+### Chapter 4: Frequency Domain
+
+- **FFT/DFT spectrum view** shows magnitude, log magnitude, or phase of the image in the frequency domain. Formula: `F(u,v) = DFT{f(x,y)}`.
+
+### Chapter 6: RGB Color Processing
+
+- **Convert to grayscale** uses configurable RGB weights, matching MATLAB-style luminance by default. Formula: `gray = 0.299R + 0.587G + 0.114B`.
+- **RGB channel view** displays one color channel as grayscale. Formula: show `R`, `G`, or `B`.
+
 ## API
 
 - `POST /api/images/`