Image Transforms in Hindi & English | रोबोटिक्स में डायनेमिक विश्लेषण और बल

Introduction

Image transforms convert image data between domains (mainly spatial and frequency), enabling analysis and operations that are difficult in the raw pixel domain. They underpin filtering, compression, reconstruction and feature extraction tasks widely used in computer vision and image processing.

Important Transforms

• Fourier Transform (FT): Converts between spatial and frequency domains; useful for filtering, convolution acceleration (via FFT), and spectral analysis.
• Discrete Cosine Transform (DCT): Energy compaction property makes it ideal for image compression (JPEG).
• Wavelet Transform: Multiresolution analysis suitable for denoising, compression (JPEG2000), and texture analysis.
• Radon Transform: Fundamental for tomography and projection-based reconstruction.
• Hough Transform: Detects shapes (lines, circles) via parametric voting in transform space.

DFT and Convolution

Using the convolution theorem, spatial convolution can be computed efficiently via multiplication in the frequency domain—critical for large-kernel filtering and some image restoration tasks.

DCT and Compression

JPEG uses blockwise DCT to concentrate energy into low-frequency coefficients, followed by quantization and entropy coding. DCT minimizes visual artifacts while achieving good compression ratios.

Wavelets

Wavelets provide localized frequency analysis across scales. They are widely used in denoising (thresholding wavelet coefficients), multi-scale feature extraction and image fusion.

Hough Transform

HT maps edge evidence from image space into parameter space (e.g., slope-intercept or Hesse normal form for lines). Peaks in parameter space correspond to shapes present in the image. The method tolerates gaps and noise in edge detections.

Applications

• Denoising and enhancement
• Image and video compression
• Tomographic reconstruction
• Feature detection and matching
• Texture and pattern recognition

Limitations

• Computational expense (large transforms)
• Artifacts introduced by block-based transforms
• Need for careful parameter tuning (thresholds, filter design)

Conclusion

Mastering image transforms equips engineers to design sophisticated vision pipelines for denoising, compression, reconstruction and recognition. Each transform provides unique strengths; selecting the right one depends on the task constraints and performance trade-offs.