Predict Image loose saturation compare to the original after training

Question

I am trying super-resolution of RGB images. I have an input image and output image I run the model and when I predict the output the image loses saturation compared to the original image. The image size is 512x512 pixels. I don't know if this happening because of the conv2dtranspose layer.

ImagedataGenerator

tf.config.run_functions_eagerly(True)
import os
import numpy as np
import cv2
def generator(idir,odir,batch_size,shuffle ):
    i_list=os.listdir(idir)
    o_list=os.listdir(odir)
    batch_index=0
    batch_size = batch_size
    sample_count=len(i_list)
    while True:        
        input_image_batch=[]
        output_image_batch=[]
        i_list=os.listdir(idir)
        o_list=os.listdir(odir)
        batch_size = batch_size
        sample_count=len(i_list)
        for i in range(batch_index * batch_size, (batch_index + 1) * batch_size  ): 
        #iterate for  a batch
            j=i % sample_count # cycle j value over range of available  images
            k=j % batch_size  # cycle k value over batch size
            if shuffle == True: # if shuffle select a random integer between 0 and sample_count-1 to pick as the image=label pair
                m=np.random.randint(low=0, high=sample_count-1, size=None, dtype=int) 
            else:
                m=j
            path_to_in_img=os.path.join(idir,i_list[m])
            path_to_out_img=os.path.join(odir,i_list[m])
            input_image=cv2.imread(path_to_in_img)
            input_image=cv2.resize(input_image,(512,512))
            input_image = cv2.cvtColor(input_image,cv2.COLOR_BGR2RGB)#create the target image from the input image 
            output_image=cv2.imread(path_to_out_img)
            output_image=cv2.resize(output_image,(512,512))
            output_image = cv2.cvtColor(output_image,cv2.COLOR_BGR2RGB)
            input_image_batch.append(input_image)
            output_image_batch.append(output_image)
                    
        input_image_array=np.array(input_image_batch) 
        input_image_array = input_image_array / 255.0
        output_image_array=np.array(output_image_batch)
        output_image_array = output_image_array / 255.0
        
        batch_index= batch_index + 1 
        yield (input_image_array, output_image_array)
        if batch_index * batch_size > sample_count:
                batch_index=0

Model

from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda

from keras.metrics import MeanIoU

kernel_initializer =  'he_uniform'  # also try 'he_normal' but model not converging... 


################################################################
def simple_unet_model(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS):
#Build the model
    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
    #s = Lambda(lambda x: x / 255)(inputs)   #No need for this if we normalize our inputs beforehand
    s = inputs

    #Contraction path
    c1 = Conv2D(16, (3, 3), kernel_initializer = kernel_initializer,activation='relu',padding='same')(s)
    c1 = Dropout(0.5)(c1)
    p1 = MaxPooling2D((2, 2))(c1)
    
    c2 = Conv2D(32, (3, 3), kernel_initializer = kernel_initializer,activation='relu',padding='same')(p1)
    c2 = Dropout(0.5)(c2)
    p2 = MaxPooling2D((2, 2))(c2)
     
    c3 = Conv2D(64, (3, 3), kernel_initializer = kernel_initializer,activation='relu', padding='same')(p2)
    c3 = Dropout(0.5)(c3)
    p3 = MaxPooling2D((2, 2))(c3)
     
    c4 = Conv2D(128, (3, 3), kernel_initializer = kernel_initializer, activation='relu', padding='same')(p3)
    c4 = Dropout(0.5)(c4)
    p4 = MaxPooling2D(pool_size=(2, 2))(c4)
     
    c5 = Conv2D(256, (3, 3), kernel_initializer = kernel_initializer,activation='relu', padding='same')(p4)
    c5 = Dropout(0.5)(c5)
    
    #Expansive path 
    u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)
    u6 = concatenate([u6, c4])
    c6 = Conv2D(128, (3, 3),kernel_initializer = kernel_initializer,activation='relu', padding='same')(u6)
    c6 = Dropout(0.5)(c6)
    
    u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)
    u7 = concatenate([u7, c3])
    c7 = Conv2D(64, (3, 3),kernel_initializer = kernel_initializer,activation='relu',  padding='same')(u7)
    c7 = Dropout(0.5)(c7)
    
    u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)
    u8 = concatenate([u8, c2])
    c8 = Conv2D(32, (3, 3),kernel_initializer = kernel_initializer,activation='relu',  padding='same')(u8)
    c8 = Dropout(0.5)(c8)
    
    u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)
    u9 = concatenate([u9, c1], axis=3)
    c9 = Conv2D(16, (3, 3),kernel_initializer = kernel_initializer,activation='relu', padding='same')(u9)
    c9 = Dropout(0.5)(c9)
    outputs = Conv2D(3, (1, 1), activation='sigmoid')(c9)

     
    model = Model(inputs, outputs)
 
  
    model.summary()
    
    return model

model.compile(optimizer='adam',loss='mean_squared_error',metrics=[psnr,ssim,tf.losses.mean_squared_error])