# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 3 The Proposed Approach
### New Program Structure Based on Information Transmission
#### 3.2.1 New Function and Terminal Set
Table 9 displays the function set used from the feature extraction layer to the classification layer in the feature construction and classification stages. Functions of the image preprocessing layer: The image preprocessing layer integrates a variety of image filtering and enhancement functions. Processing image data through these functions enhances image quality. Each function accepts either a raw image or a previously processed image, along with related parameters if required, as input. It then produces an output image, processed and of the same size as the input. Functions of the region extraction layer: The primary function of the region extraction layer is to extract a significant region from image data, reducing the computational load for irrelevant information within the image data. The region extraction layer accomplishes its function through the _Region_R_ and _Region_S_ functions. The input data for these functions include either raw image data or preprocessed image data, as well as _Coordinate_ and _Size_. Based on _Coordinate_ and _Size_, the function extracts rectangular or square regions from the image data to serve as input for subsequent processing layers. Functions of the image fusion layer: The primary function of the image fusion layer is to merge information from multiple image regions, aiming to create a new image region enriched with more comprehensive information. These functions are capable of processing two image regions of possibly different sizes. They use the size of the larger image region as a reference and apply bilinear interpolation to unify the sizes. Subsequent pixel-level fusion is performed to generate a new image region with a greater amount of information. The _Min_F_ function merges by selecting the minimum value of corresponding pixels from both image regions, thus creating a new image region. The _Median_F_ function fuses by \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Input** & **Output** & **Description** \\ \hline Gaussian & Image Data, \(\sigma_{1}\) & Image Data & Gaussian filter \\ GauDeriv & Image Data, \(\sigma_{1}\), \(\sigma_{1}\), \(\sigma_{2}\) & Image Data & Gaussian filter of the second derivative \\ Equalize & Image Data & Image Data & Histogram equalization \\ Region\_R [12] & Image Data, & Region & Extracting rectangular regions from image data \\  & Coordinate, Size & Region & Extracting square regions from image data \\ Region\_S [12] & Image Data, & Select the minimum value from corresponding pixel \\  & Coordinate, Size & points to achieve image fusion \\ Min\_F & 2 Region & Region & Choose the median value from corresponding pixel \\  & Median\_F & 2 Region & Region & points to achieve image fusion \\  & & & Calculate the average value of corresponding pixel \\ Mean\_F & 2 Region & Region & points to achieve image fusion \\ Max\_P & Region, K1, K2 & Tuple1 (Original Region, & Perform max pooling on the region \\  & & Downsampled Region) & \\ Ave\_P & Region, K1, K2 & Tuple1 (Original Region, & Perform average pooling on the region \\  & & Downsampled Region) & \\ Bilin\_D & Region, K1, K2 & Tuple1 (Original Region, & Perform bilinear downsampling on regions \\  & & Downsampled Region) & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Input** & **Output** & **Description** \\ \hline Feature\_E & Tuple1, m & Tuple2 (Original Region, Concatenated Features, t) & Extracting manual features and pixel features \\ Feature\_C & Tuple2, m, i & Tuple2 (Original Region, Constructed Features, Handcrafted Features) & Constructing new features \\ Transmit\_F & Tuple2 & Vector & Transmitting feature information to subsequent layers \\ Concat2 & Vector & Vector & Concatenating vectors into a single feature vector \\ SVM & Vector & Label & SVM classification algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Feature construction and classification stage function set.