  Over-fitting-based image compression requires weights compactness for
compression and fast convergence for practical use, posing challenges for deep
convolutional neural networks (CNNs) based methods. This paper presents a
simple re-parameterization method to train CNNs with reduced weights storage
and accelerated convergence. The convolution kernels are re-parameterized as a
weighted sum of discrete cosine transform (DCT) kernels enabling direct
optimization in the frequency domain. Combined with L1 regularization, the
proposed method surpasses vanilla convolutions by achieving a significantly
improved rate-distortion with low computational cost. The proposed method is
verified with extensive experiments of over-fitting-based image restoration on
various datasets, achieving up to -46.12% BD-rate on top of HEIF with only 200
iterations.
