Project Outline: Novel Image Denoising via Decomposition

1. Problem Statement

• Goal: To develop a noise reduction strategy by decomposing an image into components such as structure, texture, and noise, and then denoising only the noise component.
• Key Insight: Decompose the image to confine the noise into a particular component to ensure minimal loss in image details.

2. Proposed Approach

Hybrid Multi-Scale Decomposition (HMSD)   Employ wavelet transforms and bilateral filtering to carry out a two-step decomposition:

1. Level 1: Bilateral filtering decomposes an image into two layers.   Base Layer: Low-frequency structural information.   Detail Layer: High-frequency details and noise.
2. Level 2: Apply wavelet decomposition on the detail layer to further isolate noise.   Wavelet Subbands: High-frequency subbands contain most of the noise.

• Adaptive Thresholding: Apply spatially varying thresholds based on local variance to suppress noise while preserving edges.

3. Reconstruction: Combine the processed wavelet coefficients back into the detail layer, then combine with the base layer. Novelty:

• Combines spatial (bilateral filter) and frequency (wavelet) decompositions.
• Adaptive thresholding adapted to local image statistics.

3. Implementation Steps

1. Decomposition:
   • Use OpenCV for bilateral filtering.

• Employ PyWavelets for wavelet decomposition, say, Daubechies wavelets.

2. Noise Reduction:
   • Calculate local variance in wavelet subbands for adaptive threshold determination
   • Soft-threshold the high-frequency coefficients
3. Reconstruction:
   • Inverse wavelet transform on the denoised detail layer
   • Weighted addition with the base layer

4. Evaluation Metrics

• Quantitative: PSNR, SSIM on datasets such as BSD68 or Set12.
• Qualitative: Visual comparison with the state-of-the-art methods (e.g., BM3D, Non-Local Means).

5. Tools & Libraries

• Python: Core programming language.
• Libraries:
  • OpenCV: Bilateral filtering and image I/O.
  • PyWavelets: Wavelet decomposition.
  • scikit-image: Metrics (PSNR, SSIM).
  • NumPy/Matplotlib: Array operations and visualization.

6. Challenges & Solutions

• Over-Smoothing: Use edge-aware bilateral filtering and adaptive thresholds.
• Noise Residuals: Perform the decomposition on residual layers in an iterative way.
• Complexity: Enhance code with vectorization and parallel processing.

7. Expected Outcomes

• A denoising technique that will outperform conventional methods in terms of edge/texture preservation.
• Code repository with modular implementation for reproducibility.

8. Future Work

• Extend to video denoising or 3D medical imaging.
• Incorporate deep learning to automate parameter tuning.

Conclusion

It draws, by design, on the favorable features of both the spatial and frequency-domain decompositions for noise removal. Emphasis has gone towards the adaptive processing of high-frequency components whereby image details considered critical will not be significantly sacrificed for very superior performance of denoising.