Abstract—Haze is a phenomenon of degrading the visual quality of outdoor images by wet aerosols and particles suspended in the atmosphere under poor weather conditions. The incident light on to the object will be scattered and causes the distortion of color, contrast reduction and generate the halo artifacts. In this article, a Cascaded Heterogeneous Convolution UNet (CHC-UNet) with redefined Squeeze-and-Excitation (SE) mechanism framework is designed to reduce the adverse hazes’ effect in the images. The Heterogeneous Convolution kernels with size 3×3 and 1×1 are used in each block of UNet to extract the features at various image resolutions and to overcome excessive computation load. The adaptive residual connections preserve the original image features, prevent from overfitting problem and make the dehazing process more thorough. The SE Algorithm is re-defined to better represent the channel-wise image features with Local Feature Fusion (LFF) module. The qualitative and quantitative findings of the ablation experiments show that the suggested framework outperforms state-of-the-art methodologies in the examined literature using the benchmark I-Haze, O-Haze, and D-Hazy datasets. The proposed framework has an average improvement from 17.95 dB to 27.52 dB in Peak Signal-to-Noise Ratio (PSNR), from 0.56 to 0.87 in Structural SIMilarity (SSIM) index on retrieved images, from 4.98 to 3.12 in Naturalness Image Quality Evaluator (NIQE). Furthermore, the framework has varying degrees of improvement in the subjective visual effects of the dehazed output, and it also has the added advantage of optimum running time in comparison with the existing dehazing methods.