Denoising and Enhancement Methods for Complex Image Signals based on Deep Learning and Convolutional Neural Networks

Abstract

Image quality degrades under complex imaging environments due to factors such as mixed noise and illumination degradation. This paper proposes a denoising and enhancement method based on a deep convolutional neural network. The network employs an end-to-end fully convolutional architecture, designing dual-branch multi-scale dilated convolutional modules to collaboratively capture microscopic texture and global topological information; a dual attention mechanism of concatenated spatial and channel attention is used to achieve adaptive feature calibration; dense residual skip connections are introduced to enhance the reuse of shallow geometric information; and a composite supervision function is constructed by combining smoothing L1 loss, perceptual loss, and structural similarity loss. This method achieves a peak signal-to-noise ratio of 31.72 dB, a structural similarity of 0.905, and a perceptual distance as low as 0.101. Processing time for a single $256 \times 256$ image is 21.3 ms, and the generalization performance across datasets is stable. The proposed method effectively suppresses complex noise while preserving fine structural and texture information.